WCRE 2013 – Author Index |
Contents -
Abstracts -
Authors
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Bacchelli, Alberto |
WCRE '13-WORKSHOPS: "3rd Workshop on Mining Unstructured ..."
3rd Workshop on Mining Unstructured Data
Alberto Bacchelli, Nicolas Bettenburg, Latifa Guerrouj, and Sonia Haiduc (Delft University of Technology, Netherlands; Queen's University, Canada; Polytechnique Montréal, Canada; Florida State University, USA) Software development knowledge resides in the source code and in a number of other artefacts produced during the development process. To extract such a knowledge, past software engineering research has extensively focused on mining the source code, i.e., the final product of the development effort. Currently, we witness an emerging trend where researchers strive to exploit the information captured in artifacts such as emails and bug reports, free-form text requirements and specifications, comments and identifiers. Being often expressed in natural language, and not having a well-defined structure, the information stored in these artifacts is defined as unstructured data. Although research communities in Information Retrieval, Data Mining and Natural Language Processing have devised techniques to deal with unstructured data, these techniques are usually limited in scope (i.e., designed for English language text found in newspaper articles) and intended for use in specific scenarios, thus failing to achieve their full potential in a software development context. The workshop on Mining Unstructured Data (MUD) aims to provide a common venue for researchers and practitioners across software engineering, information retrieval and data mining research domains, to share new approaches and emerging results in mining unstructured software engineering data. Through this workshop, we aim to encourage cross-fertilization across different research domains, and to document and evolve the state of the art in mining unstructured data. @InProceedings{WCRE13p491, author = {Alberto Bacchelli and Nicolas Bettenburg and Latifa Guerrouj and Sonia Haiduc}, title = {3rd Workshop on Mining Unstructured Data}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {491--492}, doi = {}, year = {2013}, } |
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Bagge, Anya Helene |
WCRE '13-WORKSHOPS: "Workshop on Open and Original ..."
Workshop on Open and Original Problems in Software Language Engineering
Anya Helene Bagge and Vadim Zaytsev (University of Bergen, Norway; CWI, Netherlands) The OOPSLE workshop is a discussion-oriented and collaborative forum for formulating and addressing with open, unsolved and unsolvable problems in software language engineering (SLE), which is a research domain of systematic, disciplined and measurable approaches of development, evolution and maintenance of artificial languages used in software development. OOPSLE aims to serve as a think tank in selecting candidates for the open problem list, as well as other kinds of unconventional questions and definitions that do not necessarily have clear answers or solutions, thus facilitating the exposure of dark data. We also plan to formulate promising language-related challenges to organise in the future. @InProceedings{WCRE13p493, author = {Anya Helene Bagge and Vadim Zaytsev}, title = {Workshop on Open and Original Problems in Software Language Engineering}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {493--494}, doi = {}, year = {2013}, } Info |
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Bettenburg, Nicolas |
WCRE '13-WORKSHOPS: "3rd Workshop on Mining Unstructured ..."
3rd Workshop on Mining Unstructured Data
Alberto Bacchelli, Nicolas Bettenburg, Latifa Guerrouj, and Sonia Haiduc (Delft University of Technology, Netherlands; Queen's University, Canada; Polytechnique Montréal, Canada; Florida State University, USA) Software development knowledge resides in the source code and in a number of other artefacts produced during the development process. To extract such a knowledge, past software engineering research has extensively focused on mining the source code, i.e., the final product of the development effort. Currently, we witness an emerging trend where researchers strive to exploit the information captured in artifacts such as emails and bug reports, free-form text requirements and specifications, comments and identifiers. Being often expressed in natural language, and not having a well-defined structure, the information stored in these artifacts is defined as unstructured data. Although research communities in Information Retrieval, Data Mining and Natural Language Processing have devised techniques to deal with unstructured data, these techniques are usually limited in scope (i.e., designed for English language text found in newspaper articles) and intended for use in specific scenarios, thus failing to achieve their full potential in a software development context. The workshop on Mining Unstructured Data (MUD) aims to provide a common venue for researchers and practitioners across software engineering, information retrieval and data mining research domains, to share new approaches and emerging results in mining unstructured software engineering data. Through this workshop, we aim to encourage cross-fertilization across different research domains, and to document and evolve the state of the art in mining unstructured data. @InProceedings{WCRE13p491, author = {Alberto Bacchelli and Nicolas Bettenburg and Latifa Guerrouj and Sonia Haiduc}, title = {3rd Workshop on Mining Unstructured Data}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {491--492}, doi = {}, year = {2013}, } |
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Guerrouj, Latifa |
WCRE '13-WORKSHOPS: "3rd Workshop on Mining Unstructured ..."
3rd Workshop on Mining Unstructured Data
Alberto Bacchelli, Nicolas Bettenburg, Latifa Guerrouj, and Sonia Haiduc (Delft University of Technology, Netherlands; Queen's University, Canada; Polytechnique Montréal, Canada; Florida State University, USA) Software development knowledge resides in the source code and in a number of other artefacts produced during the development process. To extract such a knowledge, past software engineering research has extensively focused on mining the source code, i.e., the final product of the development effort. Currently, we witness an emerging trend where researchers strive to exploit the information captured in artifacts such as emails and bug reports, free-form text requirements and specifications, comments and identifiers. Being often expressed in natural language, and not having a well-defined structure, the information stored in these artifacts is defined as unstructured data. Although research communities in Information Retrieval, Data Mining and Natural Language Processing have devised techniques to deal with unstructured data, these techniques are usually limited in scope (i.e., designed for English language text found in newspaper articles) and intended for use in specific scenarios, thus failing to achieve their full potential in a software development context. The workshop on Mining Unstructured Data (MUD) aims to provide a common venue for researchers and practitioners across software engineering, information retrieval and data mining research domains, to share new approaches and emerging results in mining unstructured software engineering data. Through this workshop, we aim to encourage cross-fertilization across different research domains, and to document and evolve the state of the art in mining unstructured data. @InProceedings{WCRE13p491, author = {Alberto Bacchelli and Nicolas Bettenburg and Latifa Guerrouj and Sonia Haiduc}, title = {3rd Workshop on Mining Unstructured Data}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {491--492}, doi = {}, year = {2013}, } |
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Haiduc, Sonia |
WCRE '13-WORKSHOPS: "3rd Workshop on Mining Unstructured ..."
3rd Workshop on Mining Unstructured Data
Alberto Bacchelli, Nicolas Bettenburg, Latifa Guerrouj, and Sonia Haiduc (Delft University of Technology, Netherlands; Queen's University, Canada; Polytechnique Montréal, Canada; Florida State University, USA) Software development knowledge resides in the source code and in a number of other artefacts produced during the development process. To extract such a knowledge, past software engineering research has extensively focused on mining the source code, i.e., the final product of the development effort. Currently, we witness an emerging trend where researchers strive to exploit the information captured in artifacts such as emails and bug reports, free-form text requirements and specifications, comments and identifiers. Being often expressed in natural language, and not having a well-defined structure, the information stored in these artifacts is defined as unstructured data. Although research communities in Information Retrieval, Data Mining and Natural Language Processing have devised techniques to deal with unstructured data, these techniques are usually limited in scope (i.e., designed for English language text found in newspaper articles) and intended for use in specific scenarios, thus failing to achieve their full potential in a software development context. The workshop on Mining Unstructured Data (MUD) aims to provide a common venue for researchers and practitioners across software engineering, information retrieval and data mining research domains, to share new approaches and emerging results in mining unstructured software engineering data. Through this workshop, we aim to encourage cross-fertilization across different research domains, and to document and evolve the state of the art in mining unstructured data. @InProceedings{WCRE13p491, author = {Alberto Bacchelli and Nicolas Bettenburg and Latifa Guerrouj and Sonia Haiduc}, title = {3rd Workshop on Mining Unstructured Data}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {491--492}, doi = {}, year = {2013}, } |
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Zaytsev, Vadim |
WCRE '13-WORKSHOPS: "Workshop on Open and Original ..."
Workshop on Open and Original Problems in Software Language Engineering
Anya Helene Bagge and Vadim Zaytsev (University of Bergen, Norway; CWI, Netherlands) The OOPSLE workshop is a discussion-oriented and collaborative forum for formulating and addressing with open, unsolved and unsolvable problems in software language engineering (SLE), which is a research domain of systematic, disciplined and measurable approaches of development, evolution and maintenance of artificial languages used in software development. OOPSLE aims to serve as a think tank in selecting candidates for the open problem list, as well as other kinds of unconventional questions and definitions that do not necessarily have clear answers or solutions, thus facilitating the exposure of dark data. We also plan to formulate promising language-related challenges to organise in the future. @InProceedings{WCRE13p493, author = {Anya Helene Bagge and Vadim Zaytsev}, title = {Workshop on Open and Original Problems in Software Language Engineering}, booktitle = {Proc.\ WCRE}, publisher = {IEEE}, pages = {493--494}, doi = {}, year = {2013}, } Info |
6 authors
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